这篇论文把LLM当推理引擎，把知识存进显式数据库DaoQL，反事实推理比GPT-4o单独强49个百分点，适合做高精度场景的参考。
该论文提出数据优先本体论，将确定性知识移入显式多模态数据库DaoQL。在五个领域的反事实实验（n=1250）中，DaoQL+GPT-4o达到94%可组合反事实分解性，比单独GPT-4o高49个百分点。DaoQL在同机嵌入式设置中报告图BFS为1.20 ms、HNSW为83.1 us、Fluent混合查询为105.8 us。在LDBC SNB SF1和ANN-Benchmarks上实现34/34查询覆盖，ANN-Benchmarks在桥边保护修复后Recall@10>=99%且千级QPS。论文明确区分可证明结构、初步实证证据和架构路线图声明。
An Explicit World Model Based on Data-First Ontology: DaoQL Multimodal Storage Validation and Counterfactual Reasoning Evaluation
Large language models encode world models implicitly in neural weights, which exposes four structural risks in high-precision domains such as medicine and finance: hallucination, frozen knowledge, poor explainability, and poor modifiability. This paper proposes data-first ontology: LLMs are treated as reasoning and language engines, while deterministic knowledge is moved into an explicit multimodal database, DaoQL. We formalize an explicit world model and show that, under rule independence, deterministic evaluation, and fixed conflict resolution, explicit models provide a sufficient condition for composable counterfactual decomposability; implicit models lack atomic read/delta semantics and therefore provide no comparable architectural guarantee. The implemented system focuses on DaoQL's verified storage layer and explicit Eval path, integrating graph, column, vector, and full-text engines within one process. KVCache graph nodes, expert hot updates, and the DaoQL-Agent runtime remain future work. On an embedded same-machine setup, DaoQL reports graph BFS at 1.20 ms, HNSW at 83.1 us, and a Fluent hybrid query at 105.8 us; these results indicate engineering potential but must be interpreted with deployment-shape differences from client-server systems. Exploratory measurements on LDBC SNB SF1 and ANN-Benchmarks further show 34/34 query coverage with interactive-class queries mostly in the sub-millisecond to millisecond range, but only 1.8 QPS overall due to long-tail BI/IC queries; ANN-Benchmarks reaches Recall@10 >= 99% at thousand-level QPS after a bridge-edge protection fix. In a five-domain counterfactual experiment (n = 1250), DaoQL+GPT-4o achieves 94% composable counterfactual decomposability, 49 percentage points above GPT-4o alone. The paper explicitly separates provable structure, preliminary empirical evidence, and architectural roadmap claims.